WO2013142267A1 - Système et procédé pour faciliter une détection réflectométrique d'une activité physiologique - Google Patents

Système et procédé pour faciliter une détection réflectométrique d'une activité physiologique Download PDF

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Publication number
WO2013142267A1
WO2013142267A1 PCT/US2013/031510 US2013031510W WO2013142267A1 WO 2013142267 A1 WO2013142267 A1 WO 2013142267A1 US 2013031510 W US2013031510 W US 2013031510W WO 2013142267 A1 WO2013142267 A1 WO 2013142267A1
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Prior art keywords
data
filtering
periodicity
derived
heart rate
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PCT/US2013/031510
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English (en)
Inventor
Ronald C. Gamble
Lawrence Randolph WEILL
Steve Perry MONACOS
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Advanced Telesensors, Inc.
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Priority to EP13763607.2A priority Critical patent/EP2827766B1/fr
Publication of WO2013142267A1 publication Critical patent/WO2013142267A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
    • A61B5/0006—ECG or EEG signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/0507—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  using microwaves or terahertz waves
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition

Definitions

  • Subject matter herein relates to remote sensing systems and methods utilizing radio frequency waves to remotely measure waveforms relating to physiologic activity, such as cardiac data and respiration data.
  • An electrocardiograph is a device that is commonly used to provide information, often in the form of an electrocardiogram, concerning heart function. Electrocardiographs provide outputs that are indicative of electric fields created by the heart as it beats. Operation of an electrocardiograph typically requires attachment of nine leads, which are combined to obtain twelve sets of measurements. A large body of clinical experience has revealed correlations between specific shapes In the waveforms output by an electrocardiograph and many different types of heart conditions.
  • An impedance cardiograph is another device that is used to provide information, often in the form of an impedance cardiogram, concerning heart function.
  • Impedance cardiographs measure changes in impedance within tissue to estimate changes in volume of a subject's body and organs. When alternating currents are transmitted through a subject's chest, the impedance of the tissue in the patient's chest is altered with changes in blood volume and velocity in the aorta according to each beat of the subject's heart.
  • a phonocardiograph is a device commonly used to provide detailed information on heart sounds, usually in the form of a phonocardlogram.
  • the phonocardiogram waveform is measured by placing a sensitive microphone, or accelerometer, in contact with the chest at one of several we!l-defined auscultation locations.
  • Electrocardiographs and impedance cardiographs typically involve attaching electrical leads to the subject being measured, and impedance cardiographs typically involve passing a current through the subject's body. Phonocardiographs require attaching a specially-designed microphone or accelerometer to the subject's torso.
  • a stationary person's chest has periodic movement with no net velocity, and a continuous wave radar apparatus trained on a person's chest will receive a signal similar to the transmitted signal with its phase modulated by the time-varying chest position.
  • a signal proportion to chest position can be obtained by demodulating the phase- modulated signal.
  • U.S. Patent No. 7,81 1 ,234 discloses a non-imaging method of remotely sensing cardiac-related data of a subject, the method including: transmitting a microwave signal to illuminate tissue of the subject; receiving a reflected microwave signal, the reflected microwave signal being a reflection of the microwave signal from illuminated tissue of the subject; processing the reflected microwave signal and analyzing an amplitude of the reflected microwave signal to determine changes in a reflection coefficient at an air-tissue interface of the subject's body resulting from changes In permittivity of the illuminated tissue of the subject, the changes in permittivity containing a static component and a time-varying component; and processing the time-varying component to provide cardiographic related data of the subject.
  • the present invention relates in various aspects to systems and methods involving reflectometric detection of physiologic activity, such as (but not necessarily limited to) cardiac activity.
  • One aspect of the invention relates to a method for remotely sensing cardiac- related data of an animal subject (such as a human), the method comprising: transmitting a radio frequency signal to impinge on tissue of the subject; receiving a reflected radio frequency signal, the reflected radio frequency signal comprising a reflection of the radio frequency signal impinged on tissue of the subject; generating baseband data utilizing the reflected radio frequency signal; filtering data embodying or derived from the baseband data to yield initially filtered data, wherein said filtering includes high pass filtering with a cutoff frequency in a range of from 10 Hz or greater to yield Initially filtered data; performing waveform phase position determination on data embodying or derived from the Initially filtered data to yield waveform phase position determined data; performing at least one autocorrelation of the waveform phase position determined data to yield auto-correlated data; determining periodicity of data embodying or derived from the auto-correlated data; and computing heart rate using a maximum peak of the periodicity.
  • Another aspect of the invention relates to a system for remotely sensing cardiac- related data of an animal subject (such as a human), the system comprising: a radio frequency transmitter adapted to transmit a radio frequency signal for impingement on tissue of the subject; a radio frequency receiver adapted to receive a radio frequency signal comprising a reflection of the radio frequency signal impinged on tissue of the subject; a baseband data generating element arranged to generate baseband data from the received radio frequency signal; at least one filtering element arranged to filter data embodying or derived from the baseband data to yield initially filtered data, wherein said filtering Includes high pass filtering with a cutoff frequency in a range of from 10 Hz or greater to yield Initially filtered data; a waveform phase position determining element arranged to perform waveform phase position determination on data embodying or derived from the initially filtered data to yieid waveform phase position determined data; an auto-corre!ation element arranged to perform at least one auto-correlation of the waveform phase position determined data
  • Another aspect of the invention relates to a method for remotely sensing cardiac- related data of an animal subject (such as a human), the method comprising: transmitting a radio frequency signal to impinge on tissue of the subject; receiving a reflected radio frequency signal the reflected radio frequency signal comprising a reflection of the radio frequency signal impinged on tissue of the subject; generating baseband data utilizing the reflected radio frequency signal; filtering data embodying or derived from the baseband data to yield a plurality of sets of Initialiy filtered data, wherein each set of initially filtered data is obtained by filtering including high pass filtering with a different filtering scheme (e.g., including but not limited to different cutoff frequencies, different filter transfer function slopes, and/or presence or absence of sequential filtering steps); performing waveform phase position determination on data embodying or derived from the plurality of sets of initialiy filtered data to yield a plurality of sets of waveform phase position determined data; performing at least one auto-correlation of each set of the plurality of sets of
  • Another aspect of the invention relates to a method comprising: transmitting a radio frequency signal to impinge on tissue of an animal subject; receiving a reflected radio frequency signal, the reflected radio frequency signal comprising a reflection of the radio frequency signal impinged on tissue of the subject; generating baseband data utilizing the reflected radio frequency signal; filtering data embodying or derived from the baseband data to yield initially fiitered data, wherein said filtering includes high pass filtering with a cutoff frequency of 10 Hz or greater to yield initially filtered data; performing waveform phase position determination on data embodying or derived from the initially filtered data to yield waveform phase position determined data; determining periodicity of data embodying or derived from the waveform phase position determined data, wherein the periodicity is indicative of cardiac activity; and comparing periodicity indicative of cardiac activity for a selected interval to (i) periodicity indicative of cardiac activity for an interval preceding the selected interval and (ii) periodicity indicative of cardiac activity for an interval following the selected interval.
  • Such method may further include identifying at least one
  • any of the foregoing aspects, and/or various separate aspects and features as described herein, may be combined for additional advantage. Any of the various features and elements as disclosed herein may be combined with one or more other disclosed features and elements unless indicated to the contrary herein
  • FIG. 1 is a system interconnection diagram illustrating connections between various elements of a system for remotely sensing physiologic activity including use of refiectometric detection and signal processing.
  • FIG. 2 is a schematic illustrating radio frequency transmission and reception components according to one Implementation of the system of FIG. 1.
  • FIG. 3 is a flowchart depicting various steps of a method for processing refiectometrica!y detected signals to determine heart rate.
  • FIG. 4A is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from a first test subject according to a first analytical run over a period of 180 seconds.
  • FIG. 4B is a plot of a 10 second subset (e.g., the last 10 seconds) of the 180 second period represented In FIG. 4A.
  • FIG. 4C is a plot of a portion of data obtained from the received radio frequency signal of FIG. 4B following segmentation of the data into a seven second sample with one second intervals and following notch filtering at 60Hz and harmonics thereof.
  • FIG. 4D is a plot of the segmented data of FIG. 4C following application of a slope limiting function to reduce or eliminate aberrant peaks.
  • FIG. 4E is a plot of the data of FIG. 4D following application of at least one first bandpass filtering step.
  • FIG. 4F is a plot of the data of FIG. 4E after squaring each data point to obtain ail positive values.
  • FIG. 4G is a plot of the data of FIG. 4F following application of a second bandpass filtering step.
  • FIG. 4H Is a plot of the data of FIG. 4G following application of waveform phase position determination In the form of zero crossing detection.
  • FIG. 41 is a plot of the data of FIG. 4H following application of auto-correlation and high-pass fl!tering.
  • FIG. 4J is a plot of the data of FIG. 4I following application of a Fast Fourier Transform function to convert frequency to periodicity.
  • FIG. 4K Is a plot of heart rate of the first test subject over the 180 second period of the first analytical run derived from the reflectometric radio frequency data of FIG. 4A
  • FIG. 5A is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from a first test subject according to a second analytical run over a period of 180 seconds, with a QLGCKTM function activated.
  • FIG. 5B is a plot of a subset (e.g., time period of 162 through 174 seconds) of the
  • FIG. 5C Is a plot of a portion of data obtained from the received radio frequency signal of FIG. 5B and following segmentation of the data Into a seven second sample with one second intervals and following notch filtering at 80Hz and harmonics thereof.
  • FIG. 5D is a plot of the segmented data of FIG. 5C following application of a slope limiting function to reduce or eliminate aberrant peaks.
  • FIG. 5E is a plot of the data of FIG. 5D following application of at least one first bandpass filtering step.
  • FIG. 5F is a plot of the data of FIG. 5E after squaring each data point to obtain ail positive values.
  • FIG. 5G is a plot of the data of FIG. 5F following application of a second bandpass filtering step.
  • FIG. 5H Is a plot of the data of FIG. 5G following application of waveform phase position determination in the form of zero crossing detection.
  • FIG. 5! is a plot of the data of FIG. 5H following application of auto-correlation and high-pass filtering.
  • FIG. 5J is a plot of the data of FIG. 5I following application of a Fast Fourier Transform function to convert frequency to periodicity.
  • FIG. 5K is a plot of heart rate of the first test subject over the 180 second period of the second analytical run derived from the reflectometric radio frequency data of FIG. 5A (represented with diamond shaped data markers) in comparison to heart rate data of the first subject corresponding to the same time period run obtained from an electrocardiograph (represented with rectangular shaped data markers) applied to the first subject.
  • FIG. 6A is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from a first test subject according to a third analytical run over a period of 180 seconds.
  • FIG. 8B is a plot of a subset (e.g., the time period of 162 through 174 seconds) of the 180 second period represented in FIG. 6A.
  • FIG. 6C Is a plot of a portion of data obtained from the received radio frequency signal of FIG. 6B and following segmentation of the data into a seven second sample with one second intervals and following notch filtering at 60Hz and harmonics thereof.
  • FIG. 6D is a plot of the segmented data of FIG. 6C following application of a slope limiting function to reduce or eliminate aberrant peaks.
  • FIG. 6E is a plot of the data of FIG. 6D following application of at least one first bandpass filtering step.
  • FIG. 6F is a plot of the data of FIG. 8E after squaring each data point to obtain all positive values.
  • FIG. 8G is a plot of the data of FIG. 6F following application of a second bandpass filtering step.
  • FIG. 6H Is a plot of the data of FIG. 6G following application of waveform phase position determination In the form of zero crossing detection.
  • FIG. 8I is a plot of the data of FIG. 6H following application of auto-correlation, high-pass filtering, and half-wave rectification.
  • FIG. 6J is a plot of the data of FIG. 8I following application of a Fast Fourier Transform function to convert frequency to periodicity.
  • FIG. 6K Is a plot of heart rate of the first test subject over the 180 second period of the third analytical run derived from the reflectometric radio frequency data of FIG. 8A (represented with diamond shaped data markers) in comparison to heart rate data of the first subject corresponding to the same time period run obtained from an electrocardiograph (represented with rectangular shaped data markers) applied to the first subject.
  • FIG. 7A is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from a second test subject according to a fourth analytical run over a period of 180 seconds.
  • FIG. 7B is a plot of a subset (e.g., the time period from 184 to 178 seconds) of the 180 second period represented in FIG. 7A.
  • FIG. 7C Is a plot of a portion of data obtained from the received radio frequency signal of FIG. 7B following segmentation of the data into a seven second sample with one second Intervals and following notch filtering at 60Hz and harmonics thereof.
  • FIG. 7D is a piot of the segmented data of FIG. 7C following application of a slope !imiting function to reduce or eliminate aberrant peaks.
  • FIG. 7E is a plot of the data of FIG. 7D following application of at least one first bandpass filtering step.
  • FIG. 7F is a plot of the data of FIG. 7E after squaring each data point to obtain all positive values.
  • FIG. 7G is a plot of the data of FIG. 7F following application of a second bandpass filtering step.
  • FIG. 7H Is a p!ot of the data of FIG. 7G following application of waveform phase position determination in the form of zero crossing detection.
  • FIG. 7I is a plot of the data of FIG. 7H following application of auto-correlation, high-pass filtering, and half-wave rectification.
  • FIG. 7J is a plot of the data of FIG. 7I following application of a Fast Fourier Transform function to convert frequency to periodicity.
  • FIG. 7K is a plot of heart rate of the second test subject over the 180 second period of the third analytical run derived from the reflectometric radio frequency data of FIG. 7A (represented with diamond shaped data markers) in comparison to heart rate data of the second subject corresponding to the same time period run obtained from an electrocardiograph (represented with rectangular shaped data markers) applied to the second subject.
  • FIG. 8B is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from a third test subject according to the fifth analytical run over a period of 180 seconds.
  • FIG. 8C is a plot of the digitally converted representation of reflected raw analog radio frequency signal according to a subset (e.g., including the time period from 149 to about 178 seconds) of the 180 second period represented in FIG. 8B.
  • a subset e.g., including the time period from 149 to about 178 seconds
  • FIG. 8D is a plot of a subset (e.g., including the time period from 155 to 180 seconds) of the data of FIG. 8B following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 308-314 disclosed in connection with FIG. 3), Including (4-pole) bandpass filtering at 10-50 Hz.
  • a subset e.g., including the time period from 155 to 180 seconds
  • FIG. 8E is a plot of the same subset of data represented in FIG. 8D following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 308- 314 disclosed in conneciion with FIG. 3), but Including (4-pole) bandpass fl!tering at 10-10 Hz.
  • FIG. 8F is a plot of the same subset of data represented in FIGS. 8D-8E foiiowing application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 308- 314 disclosed in connection with FIG. 3), but including (4-po!e) bandpass filtering at 20-20 Hz.
  • FIG. 8G is a plot of the same subset of data represented in FIGS. 8D-8F following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 308- 314 disclosed in connection with FIG. 3 ⁇ , but including (4-pole) bandpass filtering at 40-40 Hz.
  • FIG. 9A is a plot (e.g., inciuding the time period from 1 15 to 140 seconds) of a reflected radio frequency signal received from the third test subject according to a sixth analytical run, foiiowing digital conversion, segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent In character with steps 306-314 disclosed in connection with FIG. 3), inciuding (4-pole) bandpass filtering at 40-40 Hz.
  • FIG. 9B is a plot Including the same reflectivity data represented in FIG. 9A following digital conversion, segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 306-314 disclosed in connection with FIG. 3), but including (4-pole) bandpass filtering at 20- 20 Hz.
  • FIG. 10B is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from the fourth third test subject according to a subset (e.g., from 155 to 180 seconds) of the seventh anaiyticai run.
  • FIG. 10C Is a plot of the same subset of data represented in FIG. 10B following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 306- 314 with steps disclosed in connection with FIG. 3), including (4-pole) bandpass filtering at 10-50 Hz.
  • FIG. 10D is a plot of the same subset of data represented in FIGS. 10A-10B following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 308- 314 with steps disclosed in connection with FIG. 3), but including (4-pole) bandpass filtering at 10-10 Hz.
  • FIG. 10E is a plot of the same subset of data represented in FIGS. 10A-10C following application of segmentation, siope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 308- 314 with steps disclosed in connection with FIG. 3), but including (4-pole) bandpass filtering at 20-20 Hz.
  • FIG. 10F is a plot of the same subset of data represented in FIGS. 10A-10E following application of segmentation, siope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 306- 314 with steps disclosed in connection with FIG. 3), but including (4-pole) bandpass filtering at 40-40 Hz.
  • FIG. 10G is a plot of a different subset (e.g., from 90 to 1 15 seconds, during which time the subject had normal respiration) of data represented in FIG. 10B following application of segmentation, siope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 306- 314 with steps disclosed in connection with FIG. 3), including (4-pole) bandpass filtering at 20-20 Hz.
  • FIG. 10H is a plot of the same subset of data represented in FIG. 10G application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 306-314 with steps disclosed in connection with FIG. 3), but including (4-pole) bandpass filtering at 40-40 Hz.
  • FIG. 101 is a plot of the same subset of data represented in FIG. 10G application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent in character with steps 306-314 with steps disclosed in connection with FIG. 3), but including (4-pole) bandpass filtering at 50-50 Hz.
  • FIG. 10J is a plot representing respiration rate for the fourth subject derived from refiectometric data during the same subset (e.g., from 90 to 1 15 seconds) of the seventh analytical run.
  • the present invention relates in various aspects to systems and methods involving refiectometric detection of physiologic activity.
  • various passages herein relate to reflectometric detection of cardiac activity (e.g., heart rate), it is to be understood that the invention Is not necessarily limited to heart rate detection, as it may be extendible to detection of respiration rate and/or other physiologic activities.
  • FIG. 1 illustrates connections between various components of a system 100 for remotely sensing physiologic activity (e.g., heart rate) of an animal subject 50.
  • physiologic activity e.g., heart rate
  • At least one RF transmitter 1 15 and at least one RF receiver 1 16 are arranged in sufficient proximity to the subject 50 to enable a RF signal from the RF transmitter 1 15 to impinge on tissue of the subject 50, and to permit a reflection of the transmitted RF signal to be received by the RF receiver 1 18.
  • Multiple RF transmitters and/or RF receivers may be used, such as may be useful to mitigate motion artifacts and/or detect multiple subjects in a sensing area.
  • RF transmitter 1 15 and RF receiver 1 16 are illustrated as being spatially separated, such components may be grouped or otherwise packaged in a single component (e.g., transceiver) or assembly.
  • the RF transmitter 1 15 and RF receiver 1 16 are arranged in communication with RF components 1 10 (as described in further detail in FIG. 2) to facilitate transmission and detection of RF signals.
  • a RF signal generated by the RF transmitter 1 15 may include a continuous wave signal, and Is preferably a microwave signal (e.g., preferably in an unregulated RF band as 900 MHz, 2.4 GHz, 5.8 GHz, or 10 GHz).
  • An analog signal received from the RF receiver 1 16 is preferably converted to a baseband signal via the RF components 1 10 and then converted to a digital signal via at least one ana!og-to-d!gita! converter 120.
  • the RF components 1 10 and analog- to-digitai converter 120 may be arranged on or in a singie substrate and/or enclosure 101 .
  • preferred embodiments include use of at least one ana!og-to-d!g!ta! converter 120, it is to be appreciated that the invention is not so limited, since one skilled in the art would appreciate that analog signals may be used and processed according to various methods disclosed herein without requiring digital conversion.
  • One or more signal processing components 130 are arranged to receive signals from the RF components 1 10 or signals derived therefrom. If signals generated by the RF components are not subject to analog-to-digital conversion, then the signal processing component(s) may include elements suitable for analog signal manipulation, such as capacitors, resistors, inductors, and transistors. In embodiments where signals from the RF components 1 10 are subjected to anaiog-to-digital conversion, the signal processing components 130 preferably embody at least one digital signal processor (processing component), such as a general purpose or special purpose microprocessor. Various functions that may be performed by one or more digital signal processors include filtering, zero-crossing detection, auto-correlation, periodicity determination, and rate computation.
  • processing component such as a general purpose or special purpose microprocessor.
  • At least one memory element 135 is preferably arranged in communication with the one or more signal processing components 130. Additionally, at least one output and/or alarm element 150, and/or a display 140, may be arranged in communication with at least one of the signal processing components 130 and/or the memory element(s) 135. Any of various components or systems (not shown) may be connected to the output/alarm element 150, such as a control system, a communications interface, and/or other functional components.
  • FIG. 2 illustrates various RF components 1 10 according to one implementation of the system 100 described in connection with FIG. 1.
  • An oscillator 1 1 1 is arranged to generate an oscillating wave signal at a desired frequency (e.g., 10 GHz ⁇ .
  • a splitter 1 12 divides the oscillating wave signal for use by the transmitting and receiving components.
  • a circulator 1 13 Is arranged to promote one-way flow (e.g., to the right) of a first split component of the oscillating wave signal toward a RF transmission signal amplifier 1 14 while attenuating any signals (e.g., noise) traveling In the opposing direction (e.g., to the left, toward the splitter 1 12).
  • An amplified oscillating wave signal generated by the amplifier 1 4 is provided to one or more multiple RF transmitting antennas 1 15A, 1 15B, of a type (e.g., microwave) appropriate to the frequency generated by the oscillator 1 1.
  • a RF receiving antenna 1 18 is arranged to receive a reflected RF signal that includes a reflection of the RF signal transmitted by the transmitting antennas 1 15A, 1 15B and reflected from tissue of an animal subject.
  • the RF signal received by the receiving antenna 1 18 is amplified by an amplifier 1 17 and then supplied to a quadrature mixer 1 18 that serves to mix at least a portion of a "transmitted" RF signal with the amplified received RF signal.
  • the quadrature mixer 1 18 receives a split portion of the oscillating wave signal following passage through the splitter 1 12 and amplification by another amplifier 1 19.
  • the reflected radio frequency signal comprises a real signal component (I) and an out-of-phase signal component (Q), wherein the quadrature mixer 1 18 is arranged to generate a baseband signal (or baseband data) that includes the real signal component (I) (via output line 1 18-1) and the out-of-phase signal component (Q) (via output line 1 18-Q).
  • the out-of-phase signal component (Q) may be kept constant (e.g., by feeding voltage from an out of phase component (Q) back to a tuned voltage of the frequency channel (e.g., via input "Vtune” associated with the oscillator 1 1 1 )), and in such embodiment the quadrature mixer 1 18 may be arranged to output a baseband signal including only the real signal component (I). Further details of a system and method for involving feeding of voltage from an out of phase component to an oscillator are disclosed in U.S. Provisional Patent Application No. 81/508,808 by Barta, G., et al., filed on July 15, 201 1 and entitled "Precision Relative
  • the RF components may be arranged to transmit an encoded signal to permit selective identification at the receiving end of signals received from the transmitter, thereby facilitating identification and removal of interfering signals.
  • Encoded signal transmission may be used in conjunction with either continuous wave or pulsed signal embodiments.
  • FIG. 3 is a flowchart depicting various steps of a method for processing refiectometrically detected signals to determine heart rate.
  • various exemplary values e.g., signal transmission frequencies, signal sampling frequencies, sample segment intervals, filtering cutoff frequencies, etc.
  • FIG. 3 shows various exemplary values (e.g., signal transmission frequencies, signal sampling frequencies, sample segment intervals, filtering cutoff frequencies, etc.)
  • an analog baseband signal either DC - 200 Hz, or high pass filtered 3-200 Hz.
  • the baseband signal may include a real signal component (!) and an out-of-phase signal component (Q), or alternatively the baseband signal may include only a real signal component (I) where the out-of-phase signal component (Q) is kept constant (QLOCKTM).
  • a second step 304 involves analog-to-digital conversion (e.g., at a sampling rate preferably in a range of 100 Hz to 10 kHz, or preferably in a range of from 250 Hz to 2 kHz, or preferably in a range of from 250 Hz to 1 .5 kHz; although a sampling rate of 1 .25 kHz is shown in FIG. 3).
  • a third method step 306 may Inciude segmenting the data (e.g., generating a 7 second sample in 1 second increments, although any suitable sample lengths and increments may be used).
  • An optional fourth method step 308 Includes application of a slope limiting function to eliminate spikes in data (e.g., where an instantaneous slope exceeds a predefined threshold value).
  • a fifth method step 310 includes filtering data embodying or derived from the baseband data to yield initially filtered data.
  • Various filtering schemes may be used, such as high-pass and/or bandpass fiite ing schemes.
  • Such filtering may preferably include digital filtering including one or more digital signal processing elements (although analog fiitering elements may alternatively be used in the absence of upstream ana!og-to-digitai conversion of baseband data).
  • such filtering preferably includes high pass filtering with a cutoff frequency in a range of from 15 Hz to 25 Hz.
  • such filtering may inciude low-pass filtering with a cutoff frequency in a range of from 15 Hz to 25 Hz and high-pass filtering with a cutoff frequency of from 15 Hz to 25 Hz, wherein the cutoff frequency of the low-pass filtering is no more than 2 Hz apart from the cutoff frequency of the high-pass filtering, and the cutoff frequency of the low-pass filtering is no greater than the cutoff frequency of the high-pass filtering.
  • such filtering may include dual bandpass filtering comprising a bandpass including low-pass filtering with a cutoff frequency of from 15 Hz to 25 Hz and including high-pass filtering with a cutoff frequency of from 15 Hz to 25 Hz, wherein the cutoff frequency of the low-pass filtering is no greater than the cutoff frequency of the high-pass filtering.
  • the dual bandpass filtering may comprise another bandpass that Includes low-pass filtering with a cutoff frequency of from 30 Hz to 70 Hz and includes high-pass filtering with a cutoff frequency of from 5 Hz to 15 Hz. As shown in FIG.
  • the fifth method step 310 may include multiple substeps such as a first substep 310A including low pass filtering with a cutoff frequency of 50 Hz (e.g., ⁇ 10 Hz) utilizing a 4-pole Butterworth filter or equivalent (e.g., a digital filtering element characterized by a transfer function having a slope along the cutoff frequency of no less than a slope of a 4-pole Butterworth filter); a second substep 310B including low pass filtering with a cutoff frequency of 20 Hz (e.g., ⁇ 5 Hz) utilizing a 4-pole Butterworth filter or equivalent; a third substep 310C including high pass filtering with a cutoff frequency of 10 Hz (e.g., ⁇ 5 Hz) utilizing a 6-pole Butterworth filter or equivalent (e.g., a digital filtering element characterized by a transfer function having a slope along the cutoff frequency of no less than a slope of a 8-poie Butterworth filter); and a fourth substeps such as
  • filters with high cutoff rates may be used. Filters having steeper cutoff slope characteristics, such as an elliptic filter according to atLab, may be used. Applying high pass filtering with a cutoff frequency of 10 Hz or greater may be useful In eliminating noise due to low frequency physiologic phenomena (e.g., including but not limited to digestive activity).
  • the method step 310 may include high pass filtering at 10 Hz (e.g., 8 pole equivalent) and low pass filtering at 50 Hz (e.g., 4 pole equivalent), In combination with additional high pass and low pass filtering steps (e.g., generally narrower bandpass filtering) with cutoff frequencies subject to adjustment.
  • additional high pass and low pass filtering steps include, but are not limited to, 10Hz-50Hz, 10Hz ⁇ 10Hz, 20Hz-20Hz, 40Hz-40Hz, and 50Hz-50Hz, each preferably having a cutoff frequency of no less than the slope of a 4-pole Butterworth filter. (Each of the preceding paired frequencies includes a low pass cutoff frequency and a high pass cutoff frequency.)
  • the method step 310 may include obtaining multiple parallel streams of data embodying or derived from the baseband data, applying different filtering schemes to different data streams of the multiple parallel data streams, and comparing results of the different filtering schemes (e.g., to select a filtering scheme providing the most reproducible physiologic monitoring result).
  • Different filtering schemes may include different cutoff frequencies, filter transfer function slopes, and/or presence or absence of sequential filtering steps, etc.
  • Such an adaptive filtering method may be used periodically (e.g., at system Initialization, when a new subject is subject to being monitored, and/or according to a fixed interval), or may be performed on a substantially continuous basis.
  • Periodicity information obtained from parallel streams of data (or data derived from periodicity data) may be used to determine heart rate, such as by selecting a majority or median of a plurality of periodicity values or values derived therefrom.
  • the filtering step 310 may be used to yield initially filtered data (with the term "initially" being used to distinguish results of any subsequent filtering steps).
  • a sixth method step 312 may include deriving either all positive values or all negative values from the Initially filtered data. Such step 312 may include, for example, squaring the initially filtered data vales, half-wave rectification, obtaining positive absolute values of the initially filtered data values, or obtaining negative absolute values of the initially filtered data values.
  • a seventh method step 314 may Include bandpass filtering the derived either all positive values or all negative values obtained from the sixth method step.
  • bandpass filtering may inciude (I) high-pass filtering with a cutoff frequency of preferably no less than 0.2 Hz, more preferably no less than 0.4 Hz, more preferably no less than 0.7 Hz, such as may be performed with a 2-po!e filter or equivalent, and (ii) low-pass filtering with a cutoff frequency of preferably no greater than 8 Hz, more preferably no greater than 5 Hz, more preferably no greater than 3 Hz, such as may be performed with a 2-pole filter or equivalent.
  • the seventh method step 314 is performed to obtain the envelope of the squaring (or positive conversion) function.
  • filter cutoff values are suitable for obtaining an envelope for sensing heart rate.
  • different filter cutoff values e.g., generally lower frequency values, such as in a range of 0.1 to 1 Hz
  • refiectometric detection of respiration rate will be substantially easier than refiectometric detection of heart rate since a reflected signal corresponding to respiration rate is at least about an order of magnitude greater than a reflected signal corresponding to heart rate.
  • An eighth method step 318 may inciude performing waveform phase position determination (such as may inciude zero-crossing detection) on data embodying or derived from the initially filtered data (e.g., on the bandpass filtered data derived from the Initially filtered data, or (optionally) directly on the initially filtered data If the sixth step 312 and seventh step 314 are omitted) to yield waveform phase position determined (e.g., zero- crossing detection) data.
  • Zero-crossing detection refers to detecting crossing of the oscillating signal through a zero value.
  • One advantage of performing waveform phase position determination is to make the resulting signal independent of power.
  • a ninth method step 318 may include auto-correlation of the waveform (data) obtained from the zero-crossing detection step.
  • Auto-correlation refers to the cross- correlation of a signal with itself (or, informally, it Is the similarity between observations as a function of the time separation between them).
  • each auto-correlation step includes multiplying a waveform by a time-shifted replicate of the same waveform.
  • An optional tenth method step 320 may include high-pass filtering with a cutoff frequency of preferably no less than 0.2 Hz, more preferably no less than 0.4 Hz, more preferably no less than 0.7 Hz, such as may be performed with a 2-pole filter or equivalent.
  • the ninth method step 318 is performed twice in sequence (constituting dual auto-correlation); in another embodiment, the ninth and tenth method steps 318, 320 are performed once and then performed again in sequence (constituting dual (auto-correlation and filtering)).
  • the purpose of the auto-correlation is to identify ring-down over the sampling period (e.g., seven seconds).
  • half-wave rectification may be performed after auto-correlation and high pass filtering to obtain positive values
  • An eleventh method step 322 may include determining periodicity of data embodying or derived from the auto-correlated data. Such determination may include performance of a Fast Fourier Transform (FFT) calculation.
  • FFT Fast Fourier Transform
  • the purpose of the eleventh step is to enable identification of the highest peak in Fourier (periodicity) space, which peak represents data with the greatest periodicity.
  • a twelfth method step 324 may include computing heart rate using a maximum peak of the periodicity. For example, if the highest peak resulting from the periodicity determination corresponds to 0.8 Hz (i.e., 0.8 cycles per second), then such data may be converted to beats per minute by multiplying the periodicity by 60 (i.e., 60 seconds per minute) to yield a value of 48 beats per minute, in certain embodiments, the computing of heart rate using a maximum peak of the periodicity includes comparing an Instantaneous heart rate value to (i) at least one previous periodicity or heart rate value, or (ii) a value derived from a plurality of previous periodicity or heart rate values, in certain embodiments, the computing of heart rate using a maximum peak of the periodicity includes comparing an instantaneous heart rate value to a median value derived from a plurality of previous periodicity or heart rate values. The resulting median value may be "stitched" together to form a continuous or substantially continuous signal.
  • the heart rate signal obtained by processing reflectometric data may include a harmonic or subharmonic of the user's actual heart rate, thereby generating a signal that may be double or half the user's actual heart rate.
  • spurious or artifact signals attributable to harmonics may be corrected by comparing the processed reflectometric data signal to prior heart rate signal data utilizing decision tree logic.
  • a thirteenth method step 326 may include outputting and/or displaying heart rate. Such output or display may be performed on a periodic or continuous basis. Heart rate data may also be stored.
  • an aspect of the invention relates to a system for remotely sensing cardiac-related data of an animal subject, the system comprising: a radio frequency transmitter adapted to transmit a radio frequency signal for impingement on tissue of the subject; a radio frequency receiver adapted to receive a radio frequency signal comprising a reflection of the radio frequency signal impinged on tissue of the subject; a baseband data generating element (e.g., comprising a quadrature mixer) arranged to generate baseband data from the received radio frequency signal; at least one filtering element arranged to filter data embodying or derived from the baseband data to yield initially filtered data, wherein said filtering includes high pass filtering with a cutoff frequency in a range of from 10 Hz or greater (e.g., including a subrange 15 Hz to 25 Hz in certain embodiments) to yield initially filtered data; a zero-crossing detection element arranged to perform zero-crossing detection on data embodying or derived from the initially filtered data to yield zero-crossing detection data
  • the foregoing system may include an ana!og-to-digltal converter arranged to convert analog baseband data to digital data prior to said filtering of data embodying or derived from the baseband data to yield initially filtered data, wherein said filtering of data comprises digital filtering.
  • the foregoing system may include at least one processor (e.g., a digital signal processor and/or a general purpose microprocessor) arranged to execute a stored machine- readable instruction set, and the at least one processor comprises one or more of the following: the at least one filtering element, the zero-crossing detection element, the autocorrelation element, the periodicity determining element, and the heart rate computing element.
  • the at least one processor may consist of a single processor comprising each of the at least one filtering element, the zero-crossing detection element, the auto-correlation element, the periodicity determining element, and the heart rate computing element.
  • a baseband data generating element may include a quadrature mixer, which may be arranged to process the received radio frequency signal with an oscillating signal representative of the transmitted radio frequency signal to output a real signal component (I) and out-of-phase signal component (Q).
  • a quadrature mixer which may be arranged to process the received radio frequency signal with an oscillating signal representative of the transmitted radio frequency signal to output a real signal component (I) and out-of-phase signal component (Q).
  • the system as described above may include a memory arranged to store at ieast one of (a) heart rate data generated by the heart rate computing element, and (b) data derived from heart rate data generated by the heart rate computing element.
  • a memory arranged to store at ieast one of (a) heart rate data generated by the heart rate computing element, and (b) data derived from heart rate data generated by the heart rate computing element.
  • Such system may further include a display element (e.g., computer monitor or other dynamically updateab!e display) arranged to display at least one of (a) heart rate data generated by the heart rate computing element, and (b) data derived from heart rate data generated by the heart rate computing element.
  • FIGS. 4A-4K, 5A-5K, and 6A-8K embody results of three analytical runs (I.e., JohnAI Q, JohnAQ, JohnAI ) performed on a first test subject
  • FIG. 7A-7K embody results of a fourth analytical run (I.e., PhiliipeAI ) performed on a second test subject.
  • the subject was seated in a resting position positioned approximately 3-6 feet from a RF transmitter and receiver.
  • FIGS. 4 ⁇ -4 ⁇ , 5A-5K, 6A-6K, and 7A-7K includes various references to the components and method steps illustrated in FIGS. 1 -3.
  • FIGS. 4A-4K provide data relating to a first analytical run performed on a first test subject ("John") utilizing a system consistent with those shown in FIGS. 1-2 and method steps consistent with those depicted in FIG. 3.
  • FIG. 4A is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from the first test subject according to a first analytical run over a period of 180 seconds, just after the test subject completed exercise (thereby starting with a high heart rate that declined over the 180 second period).
  • the operating mode "QLOCKTM” was turned off, such the reflected signal obtained from the test subject included a real signal component (I), but the out of phase signal component (Q) was not used.
  • I real signal component
  • Q out of phase signal component
  • FIG. 4A high amplitude data (real signal component (I)) is prevalent between the period of 0 to 120 seconds, but diminished thereafter.
  • FIG. 4B is a p!ot of a 10 second subset (e.g., the last 10 seconds) of the 180 second period represented in FIG. 4A.
  • FIG. 4C is a plot of a portion of data obtained from the received radio frequency signal of FIG. 4B following segmentation of the data into a seven second sample with one second intervals and following notch filtering at 60Hz and harmonics thereof (e.g., according to method steps 304, 306).
  • FIG. 4D is a plot of the segmented data of FIG. 4C following application of a slope limiting function (e.g., according to method step 308) to reduce or eliminate aberrant peaks (e.g., peaks with very high instantaneous siope).
  • a slope limiting function e.g., according to method step 308
  • FIG. 4E is a plot of the data of FIG. 4D following application of digital bandpass filtering (according to method step 310) including low-pass filtering at 50 Hz cutoff frequency (4 pole Butterworth equivalent), low pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent), high pass filtering at 10 Hz cutoff frequency (6 pole Butterworth equivalent) and high pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent).
  • MATLAB® software The athWorks, inc. was used for filter simulation and other computational functions.
  • FIG. 4F is a plot of the data of FIG. 4E after squaring each data point (according to method step 312) to obtain all positive values.
  • FIG. 4G is a plot of the data of FIG. 4F following application of a second bandpass filtering step (according to method step 314).
  • FIG. 4H is a plot of the data of FIG. 4G following application of waveform phase position determination (e.g., in the form of zero crossing detection according to method step 318). Limit thresholds of 3 x 1 Q "30 and 2ero are shown in FIG. 4H for the zero-crossing detection.
  • FIG. 4I is a plot of the data of FIG.
  • FIG. 4J is a plot of the data of FIG. 4I following application of a Fast Fourier Transform function to convert frequency to periodicity (according to method step 322). As shown in FIG. 4J, the largest amplitude peak appears at a frequency of 1.5 Hz. Such frequency corresponding to the largest amplitude peak corresponds to cardiac function. The 1.5 Hz frequency may be multiplied by 80 to convert the frequency to heart rate (90 beats per minute).
  • the computation of heart rate included comparing an instantaneous heart rate value to previous heart rate values to increment a limit on up/down values, and a median value of six beats was selected. The resulting median value obtained from each data set was "stitched" together to form a substantially continuous heart rate signal.
  • FIG. 4K Is a plot of heart rate of the first test subject over the 180 second period of the first analytical run derived from the reflectometric radio frequency data of FIG. 4A, in comparison to heart rate data of the first subject corresponding to the same time period run obtained from an electrocardiograph (EGG) applied to the first subject.
  • EGG electrocardiograph
  • FIGS. 5A-5K provide data relating to a second analytical run performed on the first test subject utilizing a system consistent with those shown in FIGS. 1-2 and method steps consistent with those depicted in FiG. 3.
  • FIG. 5A is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from the first test subject according to the second analytical run over a period of 180 seconds, just after the test subject completed exercise (thereby starting with a high heart rate that declined over the 180 second period).
  • the operating mode "QLOC TM" was turned on, such that the reflected signal obtained from the test subject included only real signal component (I) with the out-of- phase signal component (Q) kept constant. As shown in FIG.
  • FIG. 5A is a plot of the reflected raw analog radio frequency (baseband) signal received from the first test subject according to the second analytical run over a subset (e.g., time period of 162 through 174 seconds) of the 180 second period represented in FIG. 5A.
  • FIG. 5C is a plot of a portion of data obtained from the received radio frequency signal of FIG. 5B following analog to digital conversion and following segmentation of the data into a seven second sample with one second intervals (e.g., according to method steps 304, 306).
  • FIG. 5D is a plot of the segmented data of FIG. 5C following application of a slope limiting function (e.g., according to method step 308) to reduce or eliminate aberrant peaks (e.g., peaks with very high instantaneous slope).
  • a slope limiting function e.g., according to method step 308
  • FIG. 5E is a plot of the data of FIG. 5D following application of digital bandpass filtering (according to method step 310) including low-pass filtering at 50 Hz cutoff frequency (4 pole Butterworth equivalent), low pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent), high pass filtering at 10 Hz cutoff frequency (6 pole Butterworth equivalent) and high pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent).
  • MATLAB® software (The athWorks, Inc.) was used for filter simulation and other computational functions.
  • FIG. 5F is a plot of the data of FIG. 5E after squaring each data point (according to method step 312) to obtain ai! positive values.
  • FIG. 5G is a plot of the data of FIG. 5F following application of a second bandpass filtering step (according to method step 314).
  • FIG. 5H is a plot of the data of FIG. 5G following application of a zero crossing detection (according to method step 316). Limit thresholds of 3 x 10 j0 and zero are shown in FIG. 5H for the zero-crossing detection.
  • FIG. 5I is a plot of the data of FIG.
  • FIG. 5J is a plot of the data of FIG. 5 following application of a Fast Fourier Transform function to convert frequency to periodicity (according to method step 322). As shown in FIG. 5J, the largest amplitude peak appears at a frequency of about 1 .4 Hz. Such frequency corresponding to the largest amplitude peak corresponds to cardiac function. The 1.4 Hz frequency may be multiplied by 60 to convert the frequency to heart rate (84 beats per minute).
  • the computation of heart rate included comparing an instantaneous heart rate value to previous heart rate values to Increment a limit on up/down values, and a median value of six heats was selected. The resulting median value obtained from each data set was "stitched" together to form a substantially continuous heart rate signal.
  • FIG. 5K is a plot of heart rate of the first test subject over the 180 second period of the second analytical run derived from the reflectometric radio frequency data of FIG. 5A, in comparison to heart rate data of the first subject corresponding to the same time period run obtained from an electrocardiograph (EC-G) applied to the first subject.
  • ECG electrocardiograph
  • FIGS. 6A-6K provide data relating to a third analytical run performed on the first test subject utilizing a system consistent with those shown in FIGS. 1-2 and method steps consistent with those depicted In FIG. 3.
  • FIG. 6A Is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from the first test subject according to the third analytical run over a period of 180 seconds.
  • the operating mode "QLOCKTM” was turned off, and only the real signal component (I) of the reflected signal obtained from the test subject was used; the out of phase signal component (Q) was not used.
  • FIG. 6A high amplitude data is prevalent between the period of 0 to 120 seconds, but diminished thereafter.
  • FIG. 6B is a plot of a 10 second subset (e.g., the time period of 162 through 174 seconds) of the 180 second period represented in FIG. 6A.
  • FIG. 6C is a plot of a portion of data obtained from the received radio frequency signal of FIG. 6B following segmentation of the data into a seven second sample with one second intervals and following notch filtering at 60Hz and harmonics thereof (e.g., according to method steps 304, 308).
  • FIG. 6D is a plot of the segmented data of FIG. 8C following application of a slope limiting function (e.g., according to method step 308) to reduce or eliminate aberrant peaks (e.g., peaks with very high instantaneous slope).
  • a slope limiting function e.g., according to method step 308
  • FIG. 6E is a plot of the data of FIG. 6D following application of digital bandpass filtering (according to method step 310) including low-pass filtering at 50 Hz cutoff frequency (4 pole Butterworth equivalent), low pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent), high pass filtering at 10 Hz cutoff frequency (6 pole Butterworth equivalent) and high pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent).
  • MATLAB® software (The athWorks, Inc.) was used for filter simulation and other computational functions.
  • FIG. 6F is a plot of me data of FIG. 8E after squaring each data point (according to method step 312) to obtain ai! positive values.
  • FIG. 8G is a plot of the data of FIG.
  • FIG. 6F is a plot of the data of FIG. 8G following application of a zero crossing detection (according to method step 318). Limit thresholds of 3 x 10 "30 and zero are shown in FIG. 8H for the zero-crossing detection.
  • FIG. 6I is a plot of the data of FIG. 8H following application of auto-correlation and high-pass filtering (with the auto-correlation and high-pass filtering performed according to method steps 318, 320.
  • LFIG. 6J is a plot of the data of FIG. 6i following application of a Fast Fourier Transform function to convert frequency to periodicity (according to method step 322). As shown in FIG.
  • the largest amplitude peak appears at a frequency of about 1 .25 Hz. Such frequency corresponding to the largest amplitude peak corresponds to cardiac function.
  • the 1 .25 Hz frequency may be multiplied by 60 to convert the frequency to heart rate (75 beats per minute).
  • the computation of heart rate included comparing an instantaneous heart rate value to previous heart rate values to Increment a limit on up/down values, and a median value of six beats was selected. The resulting median value obtained from each data set was "stitched" together to form a substantially continuous heart rate signal.
  • FIG. 6K Is a plot of heart rate of the first test subject over the 180 second period of the third analytical run derived from the reflectometric radio frequency data of FIG. 6A, in comparison to heart rate data of the first subject corresponding to the same time period run obtained from an electrocardiograph (ECG) applied to the first subject.
  • ECG electrocardiograph
  • the heart rate derived from reflectometric detection corresponded closely to the EGG heart rate over the majority of the 180 second test period, with the reflectometrlcaliy detected heart rate being slightly higher than the EGG heart rate over substantially the entire test period.
  • FIGS. 7A-7K provide data relating to a fourth analytical run performed on a second test subject ("Phllllpe") utilizing a system consistent with those shown in FIGS. 1-2 and method steps consistent with those depicted in FIG. 3.
  • FIG. 7A is a plot of the reflected raw analog radio frequency signal received from the second test subject according to a fourth analytical run over a period of 180 seconds.
  • the operating mode "QLOCKTM” was turned off, with only the real signal component (I) of the reflected signal obtained from the test subject being used; the out of phase signal component (Q) was not used.
  • FIG. 7B is a plot of a digitally converted representation of a subset (e.g., the time period from 164 to 178 seconds) of the 180 second period represented in FIG. 7A.
  • FIG. 7C is a plot of a portion of data obtained from the received radio frequency signal of FIG. 7B and following segmentation of the data into a seven second sample with one second intervals and following notch filtering at 50Hz and harmonics thereof (e.g., according to method steps 304, 306).
  • FIG. 7D is a piot of the segmented data of FIG. 7C following application of a slope limiting function (e.g., according to method step 308) to reduce or eliminate aberrant peaks (e.g., peaks with very high Instantaneous slope).
  • a slope limiting function e.g., according to method step 308
  • FIG. 7E is a plot of the data of FIG. 7D following application of digital bandpass filtering (according to method step 310) including low-pass filtering at 50 Hz cutoff frequency (4 pole Butterworth equivalent), low pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent), high pass filtering at 10 Hz cutoff frequency (6 pole Butterworth equivalent) and high pass filtering at 20 Hz cutoff frequency (4 pole Butterworth equivalent).
  • ATLAB® software (The MathWorks, Inc.) was used for filter simulation and other computational functions.
  • FIG. 7F is a plot of the data of FIG. 7E after squaring each data point (according to method step 312) to obtain all positive values.
  • FIG. 7G is a plot of the data of FIG. 7F following application of a second bandpass filtering step (according to method step 314).
  • FIG. 7H is a plot of the data of FIG. 7G following application of a zero crossing detection (according to method step 316). Limit thresholds of 3 x 10 ⁇ 30 and zero are shown in FIG. 7H for the zero-crossing detection.
  • FIG. 7! is a plot of the data of FIG.
  • FIG. 7J is a piot of the data of FIG. 7I following application of a Fast Fourier Transform function to convert frequency to periodicity (according to method step 322).
  • the largest amplitude peak appears at a frequency of about 1 .20 Hz.
  • Such frequency corresponding to the largest amplitude peak corresponds to cardiac function.
  • the 1 .20 Hz frequency may be multiplied by 60 to convert the frequency to heart rate (72 beats per minute).
  • the computation of heart rate included comparing an instantaneous heart rate value to previous heart rate values to Increment a limit on up/down values, and a median value of six beats was selected. The resulting median value obtained from each data set was "stitched" together to form a substantially continuous heart rate signal.
  • FIG. 7K Is a plot of heart rate of the second test subject over the 180 second period of the fourth analytical run derived from the refiectometric radio frequency data of FIG. 7A, in comparison to heart rate data of the second subject corresponding to the same time period run obtained from an electrocardiograph (ECG) applied to the first subject.
  • ECG electrocardiograph
  • the heart rate derived from refiectometric detection corresponded fairly well to the ECG heart rate over the majority of the 180 second test period, with an overshoot between the time period of from 1 18 to 140 seconds, and a smaller overshoots during other intervals.
  • the refiectometrica!ly detected heart rate was slightly higher than the ECG heart rate over certain time periods, but slightly lower than the ECG heart rate over other time periods.
  • cardiac activity e.g., heart rate
  • the invention is not necessarily limited to heart rate detection, as it may be extendible to detection of respiration rate and/or other physiologic activities.
  • systems and methods disclosed herein may also be used for screening candidates for potential heart abnormalities, with the results of such screening possibly being used as a basis for applying additional diagnostic tests and/or therapeutic treatment.
  • a method (e.g., as may be used to identify potential heart abnormalities) includes: transmitting a radio frequency signal to impinge on tissue of an animal subject; receiving a reflected radio frequency signal, the reflected radio frequency signal comprising a reflection of the radio frequency signal impinged on tissue of the subject; generating baseband data utilizing the reflected radio frequency signal; filtering data embodying or derived from the baseband data to yield initially filtered data, wherein said filtering includes high pass filtering with a cutoff frequency of 10 H2 or greater to yield Initially filtered data; performing waveform phase position determination on data embodying or derived from the initially filtered data to yield waveform phase position determined data; determining periodicity of data embodying or derived from the waveform phase position determined data, wherein the periodicity is indicative of cardiac activity; and comparing periodicity indicative of cardiac activity for a selected interval to (i) periodicity indicative of cardiac activity for an interval preceding the selected interval and (ii) periodicity indicative of cardiac activity for an interval following the
  • At least one temporal variation in periodicity indicative of cardiac activity may be identified based on the results of such comparison.
  • Such temporal variations may be indicative of premature heartbeats or delayed (or weak/missed) heartbeats, (it is noted that auto-correlation of the waveform phase position determined data is not required by the preceding method, but couid optionally be performed.)
  • muitipie filtering schemes may be performed to yield multiple sets of filtered data (e.g., wherein each set of initially filtered data is obtained by filtering including high pass filtering (or bandpass filtering) with a different filtering scheme).
  • Each different filtering scheme may include at least one of (a) a different cutoff frequency, (b) a different filter transfer function slope, and (c) differing presence or absence of sequential filtering steps.
  • the performance of waveform phase position determination on data embodying or derived from the initially filtered data may yield multiple sets of waveform phase position determined data.
  • the determination of periodicity of data embodying or derived from the waveform phase position determined data may include determining periodicity of at least one set of data embodying or derived from the multiple sets of waveform phase position determined data.
  • the use of multiple filtering schemes e.g., substantially simultaneously and/or in parallel
  • reflectometric sensing of cardiac- related data of an animal subject may be advantageously used as an initial screening for potential cardiac abnormalities, and then using results of such screening to determine whether to perform additional (e.g., more invasive and/or expensive) tests to determine whether the animal subject may have a serious condition meriting treatment and/or behavioral modification.
  • At least one additional cardiac-related test may be performed on or otherwise administered to the animal subject based on identification of a potential cardiac abnormality (e.g., which may be identified based at least in part on identification of temporal variation in periodicity indicative of cardiac activity and/or corresponding signal amplitude variation.
  • An additional cardiac-related test may include, but not be limited to, one or more of the following: a blood test, an electrocardiographic test, an impedance cardiography test, an echocardiography test, a phonocardiographic test, cardiac cathe ization imaging, an exercise stress test, and a pharmacological test.
  • results of reflectometric sensing of cardiac-related data of an animal subject may be stored (e.g., electronically) to provide a basis for comparing subsequently-obtained reflectometric data for the same subject - such as may be useful to establish a baseline condition and to detect changes relative to the baseline condition with respect to time.
  • results of ref!ectometric sensing of cardiac-related data may be stored for an animal population (e.g., including a human population), optionally segregated according to any desirable criteria (e.g., age, gender, ethnicity, pre-existing health condition(s)) , and the resulting data may be used as basis for comparing data subsequently obtained for an individual animal subject or groups of animal subjects.
  • FIGS. 8A-8G embody results of a fifth analytical run (i.e., RonG1 1 ) performed on a third test subject
  • FIGS. 9A-9B embody results of a sixth analytical run (i.e., RonGS) performed on the third test subject.
  • the third test subject was previously diagnosed with heart abnormalities, and had an implanted automatic defibrillation device.
  • FIG. 8B is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from a third test subject according to the fifth analytical run over a period of 180 seconds.
  • FIG. 8C is a plot of the digitally converted representation of reflected raw analog radio frequency signal according to a subset (e.g., including the time period from 149 to about 178 seconds) of the 180 second period represented in FIG. 8B.
  • FIGS. 8D-8G all embody plots of the same subset of data (e.g., including the time period from 155 to 180 seconds) of the data of FIG. 8B following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent with steps disclosed in connection with FIG. 3); however, different bandpass filtering schemes were performed in FIGS. 8D-8G.
  • FIG. 8D represents data obtained by bandpass filtering at 10-50Hz (i.e., high-pass filtering at 10 Hz cutoff frequency and low-pass filtering at 50 Hz. cutoff frequency (with both filters being 4 pole Butterworth equivalent), FIG.
  • FIG. 8E represents data obtained by bandpass filtering at 10-10 Hz (both high and low pass at 10 Hz cutoff frequency, with both filters being 4 pole Butterworth equivalent)
  • FIG. 8F represents data obtained by bandpass filtering at 20- 20 Hz (both high and low pass at 20 Hz cutoff frequency, with both filters being 4 pole Butterworth equivalent)
  • FIG. 8G represents data obtained by bandpass filtering at 40- 40 Hz (both high and low pass at 40 Hz cutoff frequency, with both filters being 4 pole Butterworth equivalent).
  • FIGS. 8D-8E provide improved correlation to heart activity relative to FIGS. 8B-8C. Still further clarity of heart activity is visible in the plots of FIGS. 8F-8G (embodying 20-20 Hz and 40-40 Hz bandpass filtering, respectively), with notable variation In periodicity of (or time period between) heart beats as well as amplitude variations.
  • FIGS. 8F-8G are annotated with dashed lines (parallel to the y- axis) and letters corresponding to areas of Interest.
  • a first premature heartbeat i.e., a beat occurring in a shorter time interval than time intervals of preceding and subsequent beats
  • a second premature beat is visible (e.g., by closer proximity to the preceding beat than the majority of beats preceding and following the beat in question).
  • a first weak or potentially missed beat e.g., characterized by dramatically reduced amplitude in FIG. 8G appears.
  • FIGS. 8D-8G demonstrate that different filtering schemes may differently reveal cardiac abnormalities. Accordingly, it is contemplated to perform multiple filtering (e.g., high pass or bandpass filtering) schemes on the same set of reflectometric data, and it is further contemplated in certain embodiments to use and/or compare results of multiple filtering schemes to confirm detection of one or more abnormalities.
  • multiple filtering e.g., high pass or bandpass filtering
  • FIGS. 9A-9B embody results of a sixth analytical run (i.e., RonG3) performed on the third test subject while sitting in a relaxed position and with normal respiration
  • FIG. 9A is a plot (e.g., including the time period from 1 15 to 140 seconds) of a reflected radio frequency signal received from the third test subject according to a sixth analytical run, following digital conversion, segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass fiiiering steps (consistent with steps disclosed in connection with FIG. 3), including (4-poie) bandpass filtering at 40-40 Hz.
  • FIG. 9B is a plot of the same reflectivity data processed in substantially the same way as in FIG.
  • FIGS. 9A-9B have been annotated to show a baseline interval or time period t 0 representing a baseline or 'normal' time period between heartbeats, to show a first abnormal interval or time period ti (that is substantially shorter than t 0 ) representing a reduced time period between heartbeats (Indicating a premature heartbeat), and to show a second abnormal interval or time period t 2 (that is substantially longer than t 0 ) representing an increased time period between heartbeats (indicating a delayed heartbeat or weak/skipped beat).
  • refiectometric signals may be used for obtaining cardiac and/or respiratory information for animal subjects. Since surface motion of a subject due to respiration is substantially greater than motion of the same subject due to cardiac function (with resulting refiectometric signal amplitude due to respiration generally being much larger than signal amplitude due to cardiac function), it is more challenging to identify (or extract) cardiac information than to sense respiratory information, it has been demonstrated in connection with FIGS. 4A-9B that cardiac information may be obtained from refiectometric signals using various signal processing techniques. F!GS.
  • FIGS. 10A-10J show that cardiac related information may be obtained whether or not the subject is undergoing respiration.
  • FIGS. 10A-10J embody results of a seventh analytical run (i.e., Paolol ) performed on a healthy fourth test subject, wherein the fourth subject had no respiration during the final 30 seconds of the 180 second seventh analytical run (with FIG. 10J including a plot representing respiration of the subject rate during a subset during the period from 90 seconds to 1 15 seconds of the seventh analytical run).
  • a seventh analytical run i.e., Paolol
  • FIG. 10J including a plot representing respiration of the subject rate during a subset during the period from 90 seconds to 1 15 seconds of the seventh analytical run.
  • FIG. 10B is a plot of a digitally converted representation of the reflected raw analog radio frequency signal received from the fourth third test subject according to a subset (e.g., from 155 seconds to 180 seconds) of the seventh analytical run.
  • F!GS. 10C-10F embody plots of the same subset of refiectometric data represented in FIG.
  • FIGS. 1 QC-1 QF represents data following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent with steps disclosed in connection with FIG. 3); however, FIG. 10C includes (4-pole) bandpass filtering at 10-50 Hz, FIG. 10D includes (4- poie) bandpass filtering at 10-10 Hz, FIG. 10E includes (4-po!e) bandpass filtering at 20-20 Hz, and FIG. 10F includes (4-pole) bandpass filtering at 40-40 Hz. Since the fourth subject had no respiration during the period including 155 seconds to 180 seconds of the seventh analytical run, the ref!ectometric signals obtained during such time period are understood to encompass primarily cardiac related data.
  • FIGS. 10G-10I include plots of a subset of data of the seventh analytical run for the time period from 90 seconds to 1 15 seconds (i.e., while the fourth subject was undergoing normal respiration), following signal processing according to substantially the same steps but varying with respect to the bandpass filtering scheme.
  • Each of FIGS. 10G- 101 represents data following application of segmentation, slope limiting, bandpass filtering, signal squaring, and subsequent low-pass and high-pass filtering steps (consistent with steps disclosed in connection with FIG. 3); however, FIG. 10G includes (4-poie) bandpass filtering at 20-20 Hz, FIG. 10H Includes (4-pole) bandpass filtering at 40-40 Hz, and FIG.
  • FIG. 101 includes (4-po!e) bandpass filtering at 50-50 Hz. No abnormalities (e.g., as might be indicated by temporal variation in periodicity, such as may indicate premature heartbeats or delayed (or weak/missed heartbeats), were detected.
  • FIG. 10J is a plot representing respiration rate for the fourth subject derived from reflectometric data during the same subset (e.g., from 90 to 15 seconds) of the seventh analytical run. The time scales of FIG. 101 and FIG. 10J are aligned or registered with one another to permit comparison of cardiac and respiratory data.
  • FIGS. 10A-10J therefore demonstrate that reflectometric detection may be used to detect cardiac information independent of respiration rate, and/or to detect respiration rate.
  • Systems and methods as disclosed herein may provide one or more of the following beneficial technical effects: enhanced sensitivity in contact!ess sensing of physiologic (e.g., cardiac and/or respiratory) information of an animal subject; enhanced reliability in contactless sensing of physiologic (e.g., cardiac and/or respiratory) information of an animal subject; and enablement of rapid, convenient, and low-cost screening of animal subjects for potential abnormalities in cardiac function.
  • physiologic e.g., cardiac and/or respiratory

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Abstract

L'invention concerne un procédé pour la détection à distance de données cardiaques d'un sujet animal, comprenant la transmission d'un signal RF pour être incident sur un tissu du sujet, la réception d'une partie réfléchie du signal RF, la génération de données de bande de base, le filtrage de données de bande de base (par exemple comprenant un filtrage passe-haut), la réalisation d'une détermination de position de phase de forme d'onde, la réalisation d'au moins une auto-corrélation des données déterminées par une position de phase de forme d'onde, la détermination de la périodicité des données auto-corrélées, et (i) le calcul de la fréquence cardiaque à l'aide d'une périodicité maximale de la périodicité ou (ii) l'identification d'anomalies dans la fonction cardiaque, telles qu'elles peuvent être indiquées par des variations temporelles de la fréquence cardiaque et/ou une amplitude de signal correspondant à l'activité cardiaque. Des schémas de filtrage de bandes passantes multiples peuvent être utilisés.
PCT/US2013/031510 2012-03-19 2013-03-14 Système et procédé pour faciliter une détection réflectométrique d'une activité physiologique WO2013142267A1 (fr)

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